{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "Sj7-5FCPQE2s",
   "metadata": {
    "id": "Sj7-5FCPQE2s"
   },
   "source": [
    "# Домашнее задание к уроку \"Сегментация и детекция объектов\"\n",
    "\n",
    "Распознавание рукописного ввода на примере базы MNIST.\n",
    "\n",
    "Построить классификатор изображений рукописного ввода на базе MNIST (https://www.kaggle.com/c/digit-recognizer).\n",
    "\n",
    "В качестве модели классификатора можно использовать любую известную модель, за исключением сверточных нейронных сетей.\n",
    "\n",
    "Критерием качества классификатора является метрика accuracy. Для получения зачета по данной работе, значение метрики accuracy должно быть больше 0.6. Метрика оценивается на тестовой выборке в рамках контеста Digit Recognizer на Kaggle."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1396b649",
   "metadata": {
    "id": "1396b649"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import cv2\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.metrics import accuracy_score,plot_confusion_matrix\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython.display import Image"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1FUlBWTJRO0J",
   "metadata": {
    "id": "1FUlBWTJRO0J"
   },
   "source": [
    "Загружаем данные MNIST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "11f4dcc1",
   "metadata": {
    "id": "11f4dcc1"
   },
   "outputs": [],
   "source": [
    "train = np.loadtxt('train.csv', delimiter=',', skiprows=1, dtype=np.float32)\n",
    "test = np.loadtxt('test.csv', delimiter=',', skiprows=1, dtype=np.float32)\n",
    "y = np.array(train[:, 0], dtype=np.int8)\n",
    "X = np.resize(train[:, 1:], (train.shape[0], 28, 28))\n",
    "X_test = np.resize(test, (test.shape[0], 28, 28))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dFiinfV3Rjmz",
   "metadata": {
    "id": "dFiinfV3Rjmz"
   },
   "source": [
    "Визуализируем данные"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "dbb02ef4",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 200
    },
    "id": "dbb02ef4",
    "outputId": "657858f1-7dff-4b90-cd02-4440294f21f3"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(20, 10))\n",
    "for i, img in enumerate(X[0:5], 1):\n",
    "    subplot = fig.add_subplot(1, 7, i)\n",
    "    plt.imshow(img, cmap='gray');\n",
    "    subplot.set_title('%s' % y[i - 1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bx3JsgA-R4CS",
   "metadata": {
    "id": "bx3JsgA-R4CS"
   },
   "source": [
    "Создадим класс для предобработки данных. В качестве признаков будем использовать гистрограмму градиентов и признаки из PCA-преобразования."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e30d21f7",
   "metadata": {
    "id": "e30d21f7"
   },
   "outputs": [],
   "source": [
    "class PrepareMNISTData:\n",
    "    \"\"\" Класс для подготовки данных к обучению модели\n",
    "    \n",
    "    Параметры\n",
    "    ---------\n",
    "    pca_components : int\n",
    "      Число главных компонент PCA для разложения признаков изображения MNIST.\n",
    "      По умолчанию 30.\n",
    "    grad_hist_bins : int\n",
    "      Количество бинов для гистраграммы градиентов.\n",
    "      По умолчанию 16.\n",
    "    \"\"\"\n",
    "    def __init__(self, pca_components=30, grad_hist_bins=16):\n",
    "      self.pca_components = pca_components\n",
    "      self._pca = PCA(n_components=self.pca_components)\n",
    "      self._grad_hist_bins = grad_hist_bins\n",
    "    \n",
    "\n",
    "    def _get_gradient_hist(self, X):\n",
    "      \"\"\" Формирование блока признаков с градиентами\n",
    "\n",
    "      Параметры\n",
    "      ---------\n",
    "      X : array\n",
    "        Изображения MNIST в формате матрицы размером (p, h, w).\n",
    "        n - количество образцов\n",
    "        h - высотка изображения\n",
    "        w - ширина изображения\n",
    "\n",
    "      Результат\n",
    "      ---------\n",
    "      X_hist : array\n",
    "        Гистограммы изображений в формате матрицы размером (n, k)\n",
    "        n - количество чисел\n",
    "        k - кол-во бинов гистограммы\n",
    "      \"\"\"\n",
    "\n",
    "      # Находим градиенты с помощью оператора Собеля\n",
    "      train_sobel_x = np.zeros_like(X)\n",
    "      train_sobel_y = np.zeros_like(X)\n",
    "      for i in range(len(X)):\n",
    "          train_sobel_x[i] = cv2.Sobel(X[i], cv2.CV_32F, dx=1, dy=0, ksize=3)\n",
    "          train_sobel_y[i] = cv2.Sobel(X[i], cv2.CV_32F, dx=0, dy=1, ksize=3)\n",
    "      train_g, train_theta = cv2.cartToPolar(train_sobel_x, train_sobel_y)\n",
    "\n",
    "      # Гистограммы вычисляются с учетом длины вектора градиента\n",
    "      X_hist = np.zeros((len(X), self._grad_hist_bins))\n",
    "      for i in range(len(X)):\n",
    "          hist, borders = np.histogram(train_theta[i],\n",
    "                                        bins=self._grad_hist_bins,\n",
    "                                        range=(0., 2. * np.pi),\n",
    "                                        weights=train_g[i])\n",
    "          X_hist[i] = hist\n",
    "          \n",
    "      # Нормализация гистограмм\n",
    "      X_hist = X_hist / np.linalg.norm(X_hist, axis=1)[:, None]\n",
    "          \n",
    "      return X_hist\n",
    "    \n",
    "    \n",
    "    def _make_pca_features(self, X, mode='train'):\n",
    "      \"\"\" Формирование блока признаков с градиентами\n",
    "\n",
    "      Параметры\n",
    "      ---------\n",
    "      X : array\n",
    "        Изображения MNIST в формате матрицы размером (p, h, w).\n",
    "        n - количество образцов\n",
    "        h - высотка изображения\n",
    "        w - ширина изображения\n",
    "      mode : str\n",
    "        Режим работы. Если mode = 'train', то перед преобразованием\n",
    "        модель PCA будет обучена на данных из X. Иначе преобразование\n",
    "        будет сделано без обучения на текущей модели.\n",
    "\n",
    "      Результат\n",
    "      ---------\n",
    "      X_pca : array\n",
    "        PCA-признаки изображений в формате матрицы размером (n, k)\n",
    "        n - количество чисел\n",
    "        k - кол-во главных компонент PCA\n",
    "      \"\"\"\n",
    "\n",
    "      mode = str(mode).lower().strip()\n",
    "\n",
    "      img_count, img_height, img_width = X.shape\n",
    "      assert img_height*img_width >= self.pca_components, \"Количество главных компонент превышает кол-во признаков изображения\"\n",
    "\n",
    "      X_pca = np.reshape(X, (img_count, -1))\n",
    "      \n",
    "      if mode == 'train':\n",
    "          X_pca = self._pca.fit_transform(X_pca)\n",
    "      else:\n",
    "          X_pca = self._pca.transform(X_pca)\n",
    "          \n",
    "      return X_pca\n",
    "\n",
    "    \n",
    "    def fit_transform(self, X):\n",
    "      \"\"\" Функция для обучения PCA и трансформации тренировочных данных \n",
    "      \n",
    "      Параметры\n",
    "      ---------\n",
    "      X : array\n",
    "        Изображения MNIST в формате матрицы размером (p, h, w).\n",
    "        n - количество образцов\n",
    "        h - высотка изображения\n",
    "        w - ширина изображения\n",
    "\n",
    "      Результат\n",
    "      ---------\n",
    "      X_prepared : array\n",
    "        Набор признаков для обучения для каждой цифры в формате\n",
    "        матрицы размером (n, k)\n",
    "        n - количество чисел\n",
    "        k - кол-во признаков чисел\n",
    "      \"\"\"\n",
    "      X = np.array(X)\n",
    "      photo_hist = self._get_gradient_hist(X)\n",
    "      pca_data = self._make_pca_features(X, mode='train')\n",
    "      return np.hstack((photo_hist, pca_data))\n",
    "    \n",
    "    \n",
    "    def transform(self, X):\n",
    "      \"\"\" Трансформация данных MNIST на обученных моделях\n",
    "\n",
    "      Параметры\n",
    "      ---------\n",
    "      X : array\n",
    "        Изображения MNIST в формате матрицы размером (p, h, w).\n",
    "        n - количество образцов\n",
    "        h - высотка изображения\n",
    "        w - ширина изображения\n",
    "\n",
    "      Результат\n",
    "      ---------\n",
    "      X_prepared : array\n",
    "        Набор признаков для обучения для каждой цифры в формате\n",
    "        матрицы размером (n, k)\n",
    "        n - количество чисел\n",
    "        k - кол-во признаков чисел\n",
    "      \"\"\"\n",
    "      photo_hist = self._get_gradient_hist(X)\n",
    "      pca_data = self._make_pca_features(X, mode='test')\n",
    "      return np.hstack((photo_hist, pca_data))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "oCqnRzNobeA-",
   "metadata": {
    "id": "oCqnRzNobeA-"
   },
   "source": [
    "Разделим данные на обучающую и валидационную части, подготовим данные для обучения через класс PrepareMNISTData"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "dcfd828a",
   "metadata": {
    "id": "dcfd828a"
   },
   "outputs": [],
   "source": [
    "X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2,\n",
    "                                                  stratify=y, random_state=42)\n",
    "mod = PrepareMNISTData(pca_components=50, grad_hist_bins=16)\n",
    "X_train_prep = mod.fit_transform(X_train)\n",
    "X_val_prep = mod.transform(X_val)\n",
    "X_test_prep =mod.transform(X_test) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "rcGHyMfNe_WW",
   "metadata": {
    "id": "rcGHyMfNe_WW"
   },
   "source": [
    "В качестве модели выберем RandomForestClassifier. Попробуем обучить модель и получить метрику accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9ab657c7",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9ab657c7",
    "outputId": "037522ee-a1e6-4541-98aa-69a73ab3a2b3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy score: 0.9570238095238095\n"
     ]
    }
   ],
   "source": [
    "ensemble = RandomForestClassifier(n_estimators=200, random_state=42)\n",
    "ensemble.fit(X_train_prep, y_train)\n",
    "y_pred = ensemble.predict(X_val_prep)\n",
    "print('accuracy score:', accuracy_score(y_val, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cqlA41Rdd6s8",
   "metadata": {
    "id": "cqlA41Rdd6s8"
   },
   "source": [
    "В результате получили достаточно высокую метрику accuracy_score. Посмотрим на матрицу ошибок."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8ebe6f47",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 279
    },
    "id": "8ebe6f47",
    "outputId": "5ecfd1a2-2d91-4f4f-db9b-9eeeecf4283c"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(ensemble, X_val_prep, y_val)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "JbZKGf4eeRaj",
   "metadata": {
    "id": "JbZKGf4eeRaj"
   },
   "source": [
    "Как видно из матрицы мы чаще всего путаем четверку с девяткой, тройку с пятеркой и восьмерку с тройкой."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "CD_ybWjhZokW",
   "metadata": {
    "id": "CD_ybWjhZokW"
   },
   "source": [
    "# Предсказания на тестовых данных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ASzP_0PTZqoh",
   "metadata": {
    "id": "ASzP_0PTZqoh"
   },
   "outputs": [],
   "source": [
    "y_pred_test = ensemble.predict(X_test_prep)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "qa0hCjsmaIyZ",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 200
    },
    "id": "qa0hCjsmaIyZ",
    "outputId": "fd7f029b-7ae0-4d11-8ae3-d6dce5e92bc3"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(20, 10))\n",
    "for i, img in enumerate(X_test[0:5], 1):\n",
    "    subplot = fig.add_subplot(1, 7, i)\n",
    "    plt.imshow(img, cmap='gray');\n",
    "    subplot.set_title('%s' % y_pred_test[i - 1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "psFstCYhaQWi",
   "metadata": {
    "id": "psFstCYhaQWi"
   },
   "source": [
    "Готовим файл для отправки"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "pUErJR7XaN1-",
   "metadata": {
    "id": "pUErJR7XaN1-"
   },
   "outputs": [],
   "source": [
    "with open('submit.txt', 'w') as dst:\n",
    "    dst.write('ImageId,Label\\n')\n",
    "    for i, p in enumerate(y_pred_test, 1):\n",
    "        dst.write('%s,%s\\n' % (i, p))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "kPTX0ANghQYI",
   "metadata": {
    "id": "kPTX0ANghQYI"
   },
   "source": [
    "Результат работы на Kaggle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "Y1IuVAHEhUdA",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 449
    },
    "id": "Y1IuVAHEhUdA",
    "outputId": "f6a76d1a-2225-4ae6-f4da-f24346fd302f"
   },
   "outputs": [
    {
     "data": {
      "image/jpeg": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(\"kaggle_result.jpg\")"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "MNIST Homework.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
